Steve Harkins, SVP Global Warehousing and Logistics shares his view on Iron Mountain’s story so far and how partnerships are proving to be the key enabler so a stronger supply chain.

The Dexory and Iron Mountain partnership explained

Iron Mountain is a global warehousing and logistics provider specialising in contract logistics, on-demand secure storage, records management and controlled‑environment facilities. The business operates across more than 60 countries with 27,000 employees, serving organisations with high regulatory, data protection or audit requirements. Its core offer spans physical document storage, digital archiving, secure transportation and compliant destruction services. In recent years, Iron Mountain has expanded into data‑centre operations and automation‑enabled warehousing to support growing digital and regulatory demands.

For retail and food supply chains, the relevance sits in controlled storage, traceability and risk‑managed asset handling. These capabilities support areas such as product testing archives, quality documentation, sensitive operational records and temperature‑dependent materials.

The partnership between Dexory and Iron Mountain represents a material step forward in how large, multi‑site operations can unlock continuous, data‑rich visibility. Iron Mountain has become the first global partner to deploy Dexory’s next‑generation autonomous robotics platform, integrating real‑time inventory scanning, AI‑driven analytics and a full digital‑twin environment across key UK warehouses. To start with, Dexory’s robot utilises a large number of vision scanners with future potential for RFID readers to navigate a warehouse continuously while read inventory levels versus live stock records in order to drive complete inventory accuracy and overall enhanced decision making.

The collaboration is grounded in a shared goal to remove latency from inventory processes. Dexory’s autonomous mobile robots capture high‑frequency, high‑accuracy data, enabling Iron Mountain to modernise operations traditionally reliant on manual checks. Early deployments focus on fast, repeatable aisle‑level scanning that feeds directly into a unified digital layer, giving Iron Mountain richer control of assets, higher accuracy and a platform to scale advanced decision‑making.

For supply chain leaders, this partnership illustrates a wider industry shift. Robotics‑enabled sensing is moving from pilot use to enterprise deployment, particularly in high‑value, high‑compliance environments such as information management, storage and asset lifecycle operations. Iron Mountain’s adoption shows that automation is now being used not only to reduce cost and risk but to build data infrastructures capable of supporting growth, diversification and stronger customer promises. A trend we can expect to see continue as the popularity  and drive for Artificial Intelligence (AI) grows wider in the industry.

What shift does this represent for supply chains?

Inventory accuracy is becoming a strategic differentiator. Continuous scanning reduces margins for error and removing the human element also removes variability. Tighter working capital and strengthened service levels across multi‑node networks are also cited as benefits.

Automation is evolving from labour substitution to decision enablement. The real value lies in the system‑wide data model, not the robot itself. Organisations that act early will build the analytics advantage needed for the next wave of optimisation.

Retail and FMCG implications are essential to consider as data precision increases, upstream and downstream partners will expect similar accuracy. Early adopters will be better positioned to handle SKU volatility, sustainability reporting demands and omnichannel complexity.

How does this support ESG and sustainability objectives?

Better inventory visibility typically reduces waste, rework and unnecessary transport movements. For food and drink industries, this means fewer date‑expired products and less destruction. For general merchandise, it reduces obsolescence and improves asset utilisation. The larger opportunity comes when digital twins are used to optimise space, energy use and network design, which can support both decarbonisation and cost agendas.

Action: Link any autonomous inventory business case directly to your ESG metrics, such as waste reduction, CO₂ per order and energy efficiency, so sustainability benefits are visible alongside financial ones.

What are the main risks or barriers to adoption?

The most material risks are operational disruptions during deployment, poor data quality undermining trust in the system, and underestimating integration complexity. There are also standard concerns around cyber security and physical safety, although modern Autonomous Mobile Robot (AMR) platforms are generally designed with robust safeguards. In many organisations, the biggest barrier is not technology, it is competing investment priorities and a lack of a clear, quantified business case.

Action: Before engaging vendors, align internally on your top three risks and mitigation measures, and insist that any pilot design directly tests those specific concerns.